# coding=utf-8 # Copyright 2024 the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from typing import Optional, Union import torch from torch import nn from transformers.models.llava_next.image_processing_llava_next_fast import LlavaNextImageProcessorFast from transformers.models.llava_next_video.modeling_llava_next_video import ( LlavaNextVideoCausalLMOutputWithPast, LlavaNextVideoForConditionalGeneration, LlavaNextVideoModel, LlavaNextVideoModelOutputWithPast, LlavaNextVideoPreTrainedModel, TransformersKwargs, get_anyres_image_grid_shape, image_size_to_num_patches, unpad_image, ) from ...cache_utils import Cache from ...image_processing_utils import BatchFeature from ...image_processing_utils_fast import DefaultFastImageProcessorKwargs, group_images_by_shape, reorder_images from ...image_utils import ( OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, ImageInput, PILImageResampling, SizeDict, get_image_size, ) from ...modeling_flash_attention_utils import FlashAttentionKwargs from ...processing_utils import Unpack from ...utils import ( TensorType, auto_docstring, can_return_tuple, is_torchvision_available, is_torchvision_v2_available, logging, ) if is_torchvision_available(): if is_torchvision_v2_available(): from torchvision.transforms.v2 import functional as F else: from torchvision.transforms import functional as F logger = logging.get_logger(__name__) class LlavaOnevisionFastImageProcessorKwargs(DefaultFastImageProcessorKwargs): """ image_grid_pinpoints (`list[list[int]]`, *optional*): A list of possible resolutions to use for processing high resolution images. The best resolution is selected based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess` method. do_pad (`bool`, *optional*): Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest number of patches in the batch. Padding will be applied to the bottom and right with zeros. """ image_grid_pinpoints: Optional[list[list[int]]] do_pad: Optional[bool] class LlavaOnevisionImageProcessorFast(LlavaNextImageProcessorFast): resample = PILImageResampling.BICUBIC image_mean = OPENAI_CLIP_MEAN image_std = OPENAI_CLIP_STD size = {"height": 384, "width": 384} crop_size = None default_to_square = False do_resize = True do_center_crop = None do_rescale = True do_normalize = True do_convert_rgb = True do_pad = True image_grid_pinpoints = [[384, 384], [384, 768], [384, 1152], [384, 1536], [384, 1920], [384, 2304], [768, 384], [768, 768], [768, 1152], [768, 1536], [768, 1920], [768, 2304], [1152, 384], [1152, 768], [1152, 1152], [1152, 1536], [1152, 1920], [1152, 2304], [1536, 384], [1536, 768], [1536, 1152], [1536, 1536], [1536, 1920], [1536, 2304], [1920, 384], [1920, 768], [1920, 1152], [1920, 1536], [1920, 1920], [1920, 2304], [2304, 384], [2304, 768], [2304, 1152], [2304, 1536], [2304, 1920], [2304, 2304]] # fmt: skip model_input_names = ["pixel_values_videos"] # Copied from transformers.models.llava.image_processing_llava_fast.LlavaImageProcessorFast.pad_to_square def pad_to_square( self, images: "torch.Tensor", background_color: Union[int, tuple[int, int, int]] = 0, ) -> "torch.Tensor": """ Pads an image to a square based on the longest edge. Args: images (`np.ndarray`): The images to pad. background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0): The color to use for the padding. Can be an integer for single channel or a tuple of integers representing for multi-channel images. If passed as integer in mutli-channel mode, it will default to `0` in subsequent channels. Returns: `torch.Tensor`: The padded images. """ height, width = get_image_size(images, ChannelDimension.FIRST) if height == width: return images num_channels = images.shape[1] if len(images.shape) == 4 else images.shape[0] if isinstance(background_color, int): background_color = [background_color] + [0] * (num_channels - 1) elif len(background_color) != num_channels: raise ValueError( f"background_color must have no more than {num_channels} elements to match the number of channels" ) max_dim = max(height, width) paste_x_left = (max_dim - width) // 2 paste_y_left = (max_dim - height) // 2 paste_x_right = max_dim - width - paste_x_left paste_y_right = max_dim - height - paste_y_left padded_images = F.pad( images, padding=[paste_x_left, paste_y_left, paste_x_right, paste_y_right], fill=background_color ) return padded_images @auto_docstring def preprocess(self, images: ImageInput, **kwargs: Unpack[LlavaOnevisionFastImageProcessorKwargs]) -> BatchFeature: if isinstance(images, (tuple, list)) and isinstance(images[0], (tuple, list)): # if the first element is a list, we assume that all elements are lists batch_num_images = [len(x) for x in images] elif isinstance(images, (tuple, list)): # treat this as a single-image case for backward compatibility batch_num_images = [1] * len(images) else: batch_num_images = [1] kwargs["batch_num_images"] = batch_num_images return super().preprocess(images, **kwargs) def _preprocess( self, images: list["torch.Tensor"], do_resize: bool, size: SizeDict, image_grid_pinpoints: list[list[int]], interpolation: Optional["F.InterpolationMode"], do_center_crop: bool, crop_size: SizeDict, do_rescale: bool, rescale_factor: float, do_normalize: bool, image_mean: Optional[Union[float, list[float]]], image_std: Optional[Union[float, list[float]]], do_pad: bool, batch_num_images: list[int], disable_grouping: Optional[bool], return_tensors: Optional[Union[str, TensorType]], **kwargs, ) -> BatchFeature: processed_images = [] image_sizes = [] # only single image patching is supported need_patching = [n == 1 for n in batch_num_images for _ in range(n)] # Determine the size tuple if size and size.height and size.width: size_tuple = (size.height, size.width) else: size_tuple = (size.shortest_edge, size.shortest_edge) # Determine the patch size if crop_size and crop_size.height: patch_size = crop_size.height elif size and size.height: patch_size = size.height else: patch_size = size.shortest_edge for i, image in enumerate(images): if need_patching[i]: image_patches = self._get_image_patches( image, image_grid_pinpoints, size=size_tuple, patch_size=patch_size, interpolation=interpolation, ) else: padded_image = self.pad_to_square( images=image, background_color=tuple(int(x * 255) for x in self.image_mean) ) image_patches = [padded_image] # Group images by size for batched processing processed_image_patches_grouped = {} grouped_image_patches, grouped_image_patches_index = group_images_by_shape( image_patches, disable_grouping=disable_grouping ) for shape, stacked_image_patches in grouped_image_patches.items(): if do_resize: stacked_image_patches = self.resize( image=stacked_image_patches, size=size, interpolation=interpolation, ) if do_center_crop: stacked_image_patches = self.center_crop(stacked_image_patches, crop_size) # Fused rescale and normalize stacked_image_patches = self.rescale_and_normalize( stacked_image_patches, do_rescale, rescale_factor, do_normalize, image_mean, image_std ) processed_image_patches_grouped[shape] = stacked_image_patches processed_image_patches = reorder_images(processed_image_patches_grouped, grouped_image_patches_index) processed_image_patches = ( torch.stack(processed_image_patches, dim=0) if return_tensors else processed_image_patches ) processed_images.append(processed_image_patches) image_sizes.append(get_image_size(image, ChannelDimension.FIRST)) if do_pad: processed_images = self._pad_for_batching(processed_images) processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images return BatchFeature( data={"pixel_values": processed_images, "image_sizes": image_sizes, "batch_num_images": batch_num_images}, tensor_type=return_tensors, ) class LlavaOnevisionModelOutputWithPast(LlavaNextVideoModelOutputWithPast): pass class LlavaOnevisionCausalLMOutputWithPast(LlavaNextVideoCausalLMOutputWithPast): pass class LlavaOnevisionPreTrainedModel(LlavaNextVideoPreTrainedModel): pass class LlavaOnevisionModel(LlavaNextVideoModel): def __init__(self, config): super().__init__(config) del self.vision_resampler def pack_image_features(self, image_features, image_sizes, image_newline=None, vision_aspect_ratio="anyres_max_9"): """ Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors. Args: image_features (`list[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`) List of image feature tensor, each contains all the visual feature of all patches. image_sizes (`torch.Tensor` of shape `(num_images, 2)`) Actual image size of each images (H, W). image_newline (`torch.Tensor` of shape `(embed_dim)`) New line embedding vector. vision_aspect_ratio (`str`, *optional*, "anyres_max_9"): Aspect ratio used when processong image features. The default value is "anyres_max_9". Returns: image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`) feature_lens (`list[int]`) token length of each image in image_features """ new_image_features = [] feature_lens = [] for image_idx, image_feature in enumerate(image_features): if image_feature.shape[0] > 1: base_image_feature = image_feature[0] image_feature = image_feature[1:] height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size if height * width != base_image_feature.shape[0]: raise ValueError("The number of patches is not consistent with the image size.") num_patch_height, num_patch_width = get_anyres_image_grid_shape( image_sizes[image_idx], self.config.image_grid_pinpoints, self.config.vision_config.image_size, ) image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1) image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous() image_feature = image_feature.flatten(1, 2).flatten(2, 3) image_feature = unpad_image(image_feature, image_sizes[image_idx]) max_num_patches = int(vision_aspect_ratio.strip("anyres_max_")) channels, curr_height, curr_width = image_feature.shape ratio = math.sqrt(curr_height * curr_width / (max_num_patches * height**2)) if ratio > 1.1: image_feature = image_feature[None] image_feature = nn.functional.interpolate( image_feature, [int(curr_height // ratio), int(curr_width // ratio)], mode="bilinear" )[0] if image_newline is not None: image_feature = torch.cat( ( image_feature, image_newline[:, None, None] .expand(*image_feature.shape[:-1], 1) .to(image_feature.device, image_feature.dtype), ), dim=-1, ) image_feature = image_feature.flatten(1, 2).transpose(0, 1) image_feature = torch.cat((base_image_feature, image_feature), dim=0) else: image_feature = image_feature[0] if image_newline is not None: image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0) image_feature = image_feature.flatten(0, 1) new_image_features.append(image_feature) feature_lens.append(image_feature.size(0)) feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features[0].device) return new_image_features, feature_lens def apply_pooling(self, image_features): height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size batch_frames, seq_len, dim = image_features.shape image_features = image_features.view(batch_frames, height, width, -1) image_features = image_features.permute(0, 3, 1, 2).contiguous() height, width = image_features.shape[2:] scaled_shape = [math.ceil(height / 2), math.ceil(width / 2)] image_features = nn.functional.interpolate(image_features, size=scaled_shape, mode="bilinear") image_features = image_features.permute(0, 2, 3, 1) image_features = image_features.view(batch_frames, -1, dim) return image_features def get_image_features( self, pixel_values: torch.FloatTensor, image_sizes: torch.Tensor, vision_feature_layer: Optional[Union[int, list[int]]] = None, vision_feature_select_strategy: Optional[str] = None, vision_aspect_ratio: Optional[str] = None, batch_num_images: Optional[torch.LongTensor] = None, ): """ Obtains image last hidden states from the vision tower and apply multimodal projection. Args: pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`) The tensors corresponding to the input images. image_sizes (`torch.Tensor` of shape `(num_images, 2)`) Actual image size of each images (H, W). vision_feature_layer (`Union[int, list[int]]`): The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. vision_feature_select_strategy (`str`): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"` batch_num_images (`torch.LongTensor`, *optional*): Number of images in each sample. Returns: image_features (list[`torch.Tensor`]): List of image feature tensor, each contains all the visual feature of all patches and are of shape `(num_patches, image_length, embed_dim)`). """ vision_feature_layer = ( vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer ) vision_feature_select_strategy = ( vision_feature_select_strategy if vision_feature_select_strategy is not None else self.config.vision_feature_select_strategy ) vision_aspect_ratio = ( vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio ) # ! infer image_num_patches from image_sizes if batch_num_images is None: # treat this as a single-image case for backward compatibility need_patching = [True] * len(image_sizes) else: need_patching = [n == 1 for n in batch_num_images for _ in range(n)] image_num_patches = [ image_size_to_num_patches( image_size=imsize, grid_pinpoints=self.config.image_grid_pinpoints, patch_size=self.config.vision_config.image_size, ) if should_patch else 1 for imsize, should_patch in zip(image_sizes, need_patching) ] if pixel_values.dim() == 5: # stacked if input is (batch_size, num_patches, num_channels, height, width) _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)] pixel_values = torch.cat(_pixel_values_list, dim=0) elif pixel_values.dim() != 4: # otherwise has to be stacked from list of (num_patches, num_channels, height, width) raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions") image_features = self.vision_tower(pixel_values, output_hidden_states=True) # If we have one vision feature layer, return the corresponding hidden states, # otherwise, select the hidden states of each feature layer and concatenate them if isinstance(vision_feature_layer, int): selected_image_feature = image_features.hidden_states[vision_feature_layer] else: hs_pool = [image_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer] selected_image_feature = torch.cat(hs_pool, dim=-1) if vision_feature_select_strategy == "default": selected_image_feature = selected_image_feature[:, 1:] elif vision_feature_select_strategy == "full": selected_image_feature = selected_image_feature image_features = self.multi_modal_projector(selected_image_feature) image_features = torch.split(image_features, image_num_patches, dim=0) image_features, feature_lens = self.pack_image_features( image_features, image_sizes, image_newline=self.image_newline, vision_aspect_ratio=vision_aspect_ratio, ) return image_features def get_video_features( self, pixel_values: torch.FloatTensor, vision_feature_layer: Union[int, list[int]], vision_feature_select_strategy: str, ): """ Obtains video last hidden states from the vision tower, apply multimodal projection and pooling. Args: pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`) The tensors corresponding to the input video. vision_feature_layer (`Union[int, list[int]], *optional*, defaults to -2`): The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. vision_feature_select_strategy (`str`): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"` Returns: video_features (list[`torch.Tensor`]): List of video feature tensor, each contains all the visual feature of all patches and are of shape `(num_videos, video_length, embed_dim)`). """ batch_size, frames, channels, height, width = pixel_values.shape pixel_values = pixel_values.view(batch_size * frames, channels, height, width) video_features = self.vision_tower(pixel_values, output_hidden_states=True) # If we have one vision feature layer, return the corresponding hidden states, # otherwise, select the hidden states of each feature layer and concatenate them if isinstance(vision_feature_layer, int): selected_video_feature = video_features.hidden_states[vision_feature_layer] else: hs_pool = [video_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer] selected_video_feature = torch.cat(hs_pool, dim=-1) if vision_feature_select_strategy == "default": selected_video_feature = selected_video_feature[:, 1:] elif vision_feature_select_strategy == "full": selected_video_feature = selected_video_feature video_features = self.multi_modal_projector(selected_video_feature) video_features = self.apply_pooling(video_features) video_features = video_features.reshape(batch_size, frames * video_features.shape[1], -1) return video_features def forward( self, input_ids: torch.LongTensor = None, pixel_values: torch.FloatTensor = None, image_sizes: Optional[torch.LongTensor] = None, pixel_values_videos: torch.FloatTensor = None, image_sizes_videos: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, vision_feature_layer: Optional[Union[int, list[int]]] = None, vision_feature_select_strategy: Optional[str] = None, vision_aspect_ratio: Optional[str] = None, batch_num_images: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> Union[tuple, LlavaOnevisionModelOutputWithPast]: r""" image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*): The sizes of the videos in the batch, being (height, width) for each frame in the video. vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`): Aspect ratio used when processong image features. The default value is "anyres_max_9". batch_num_images (`torch.LongTensor`, *optional*): Number of images in each sample. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_feature_layer = ( vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer ) vision_feature_select_strategy = ( vision_feature_select_strategy if vision_feature_select_strategy is not None else self.config.vision_feature_select_strategy ) vision_aspect_ratio = ( vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio ) if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.get_input_embeddings()(input_ids) # Images are processed with Anyres if pixel_values is not None: image_features = self.get_image_features( pixel_values, image_sizes, vision_feature_layer=vision_feature_layer, vision_feature_select_strategy=vision_feature_select_strategy, batch_num_images=batch_num_images, ) image_features = torch.cat(image_features, dim=0) image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype) special_image_mask, _ = self.get_placeholder_mask( input_ids, inputs_embeds=inputs_embeds, image_features=image_features ) inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) # Video are simply embedded and further pooled to decrease seq len if pixel_values_videos is not None: video_features = self.get_video_features( pixel_values_videos, vision_feature_layer=vision_feature_layer, vision_feature_select_strategy=vision_feature_select_strategy, ) image_newline = ( self.image_newline[None, None, :].repeat(video_features.shape[0], 1, 1).to(video_features.device) ) video_features = torch.cat((video_features, image_newline), dim=1) video_features = video_features.flatten(0, 1).to(inputs_embeds.device, inputs_embeds.dtype) _, special_video_mask = self.get_placeholder_mask( input_ids, inputs_embeds=inputs_embeds, video_features=video_features ) inputs_embeds = inputs_embeds.masked_scatter(special_video_mask, video_features) outputs = self.language_model( attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, cache_position=cache_position, **kwargs, ) return LlavaOnevisionModelOutputWithPast( last_hidden_state=outputs.last_hidden_state, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, image_hidden_states=image_features if pixel_values is not None else None, video_hidden_states=video_features if pixel_values_videos is not None else None, ) class LlavaOnevisionForConditionalGeneration(LlavaNextVideoForConditionalGeneration): @can_return_tuple @auto_docstring def forward( self, input_ids: torch.LongTensor = None, pixel_values: torch.FloatTensor = None, image_sizes: Optional[torch.LongTensor] = None, pixel_values_videos: torch.FloatTensor = None, image_sizes_videos: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, vision_feature_layer: Optional[Union[int, list[int]]] = None, vision_feature_select_strategy: Optional[str] = None, vision_aspect_ratio: Optional[str] = None, batch_num_images: Optional[torch.LongTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs: Unpack[TransformersKwargs], ) -> Union[tuple, LlavaOnevisionCausalLMOutputWithPast]: r""" image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*): The sizes of the videos in the batch, being (height, width) for each frame in the video. vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`): Aspect ratio used when processong image features. The default value is "anyres_max_9". batch_num_images (`torch.LongTensor`, *optional*): Number of images in each sample. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Example: ```python >>> from PIL import Image >>> import requests >>> import torch >>> from transformers import LlavaOnevisionProcessor, LlavaOnevisionForConditionalGeneration >>> model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype="float16", device_map="cuda:0") >>> processor = LlavaOnevisionProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf") >>> conversation = [ ... { ... "role": "user", ... "content": [ ... {"type": "text", "text": "What is shown in this image?"}, ... {"type": "image"}, ... ], ... }, ... ] >>> prompt = processor.apply_chat_template(conversation, add_generation_prompt=True) >>> image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> raw_image = Image.open(requests.get(image_file, stream=True).raw) >>> inputs = processor(text=prompt, images=raw_image, return_tensors='pt').to(0, torch.float16) >>> output = model.generate(**inputs, max_new_tokens=20, do_sample=False) >>> processor.batch_decode(output, skip_special_tokens=True)[0] "user\n\nWhat is shown in this image?\nassistant\ncat" ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_feature_layer = ( vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer ) vision_feature_select_strategy = ( vision_feature_select_strategy if vision_feature_select_strategy is not None else self.config.vision_feature_select_strategy ) vision_aspect_ratio = ( vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio ) outputs = self.model( input_ids=input_ids, pixel_values=pixel_values, pixel_values_videos=pixel_values_videos, image_sizes=image_sizes, image_sizes_videos=image_sizes_videos, vision_aspect_ratio=vision_aspect_ratio, vision_feature_layer=vision_feature_layer, vision_feature_select_strategy=vision_feature_select_strategy, batch_num_images=batch_num_images, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, cache_position=cache_position, logits_to_keep=logits_to_keep, **kwargs, ) hidden_states = outputs[0] # Only compute necessary logits, and do not upcast them to float if we are not computing the loss slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None if labels is not None: loss = self.loss_function( logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs ) return LlavaOnevisionCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, image_hidden_states=outputs.image_hidden_states, video_hidden_states=outputs.video_hidden_states, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, inputs_embeds=None, pixel_values=None, image_sizes=None, pixel_values_videos=None, image_sizes_videos=None, attention_mask=None, cache_position=None, logits_to_keep=None, **kwargs, ): # Overwritten -- in specific circumstances we don't want to forward image inputs to the model model_inputs = super().prepare_inputs_for_generation( input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask, cache_position=cache_position, logits_to_keep=logits_to_keep, **kwargs, ) if cache_position[0] == 0: # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore # Otherwise we need pixel values to be passed to model model_inputs["pixel_values"] = pixel_values model_inputs["image_sizes"] = image_sizes model_inputs["pixel_values_videos"] = pixel_values_videos model_inputs["image_sizes_videos"] = image_sizes_videos return model_inputs __all__ = [ "LlavaOnevisionImageProcessorFast", "LlavaOnevisionModel", "LlavaOnevisionForConditionalGeneration", "LlavaOnevisionPreTrainedModel", ]